def main():
result_path = "../result_toy/"
if not os.path.exists(result_path):
os.makedirs(result_path)
batch_size = 32
train = '../dataset/toy_reverse/train/'
X_train, y_train = parse_data(train)
train_ner_data = ToyDataFeeder(X_train, y_train)
train_X, train_Y, _ \
= train_ner_data.naive_batch_buckets(batch_size)
index2word = train_ner_data._idx_to_word
index2label = train_ner_data._idx_to_label
vocab_size = len(index2word)
label_size = len(index2label)
# Using word2vec pre-trained embedding
# word_embedding_dim = 300
word_embedding_dim = 8
hidden_dim = 64
label_embedding_dim = 8
max_epoch = 50
# 0.001 is a good value
learning_rate = 0.001
#attention = "fixed"
attention = None
#pretrained = 'de64'
pretrained = None
if pretrained == 'de64':
word_embedding_dim = 64
gpu = True
load_model_filename = None
machine = ner(word_embedding_dim, hidden_dim, label_embedding_dim, vocab_size, label_size, learning_rate=learning_rate, minibatch_size=batch_size, max_epoch=max_epoch, train_X=train_X, train_Y=train_Y, val_X=None, val_Y=None, test_X=None, test_Y=None, attention=attention, gpu=gpu, pretrained=pretrained, load_model_filename=load_model_filename)
if gpu:
machine = machine.cuda()
shuffle = True
# Pure training, no evaluation
train_loss_list = machine.train(shuffle, result_path, False, None)
def main():
result_path = "../result_toy/"
train = "../dataset/toy_reverse/train/"
X_train, y_train = parse_data(train)
train_ner_data = ToyDataFeeder(X_train, y_train)
batch_size = 1
val = "../dataset/toy_reverse/valid/"
test = "../dataset/toy_reverse/test/"
X_val, y_val = parse_data(val)
X_test, y_test = parse_data(test)
val_ner_data = ToyDataFeeder(X_val,
y_val,
word_to_idx=train_ner_data._word_to_idx,
idx_to_word=train_ner_data._idx_to_word,
label_to_idx=train_ner_data._label_to_idx,
idx_to_label=train_ner_data._idx_to_label)
val_X, val_Y, _ \
= val_ner_data.naive_batch_buckets(batch_size)
test_ner_data = ToyDataFeeder(X_test,
y_test,
word_to_idx=train_ner_data._word_to_idx,
idx_to_word=train_ner_data._idx_to_word,
label_to_idx=train_ner_data._label_to_idx,
idx_to_label=train_ner_data._idx_to_label)
test_X, test_Y, _ \
= test_ner_data.naive_batch_buckets(batch_size)
index2word = train_ner_data._idx_to_word
index2label = train_ner_data._idx_to_label
vocab_size = len(index2word)
label_size = len(index2label)
# Using word2vec pre-trained embedding
# word_embedding_dim = 300
word_embedding_dim = 8
hidden_dim = 64
label_embedding_dim = 8
max_epoch = 50
# 0.001 is a good value
learning_rate = 0.001
#attention = "fixed"
attention = None
#pretrained = 'de64'
pretrained = None
if pretrained == 'de64':
word_embedding_dim = 64
gpu = False
##################
eval_output_file = open(
os.path.join(result_path, "eval_beam_1_ckpt_24.txt"), "w+")
epoch = 24
load_model_filename = os.path.join(result_path,
"ckpt_" + str(epoch) + ".pth")
machine = ner(word_embedding_dim,
hidden_dim,
label_embedding_dim,
vocab_size,
label_size,
learning_rate=learning_rate,
minibatch_size=batch_size,
max_epoch=max_epoch,
train_X=None,
train_Y=None,
val_X=val_X,
val_Y=val_Y,
test_X=test_X,
test_Y=test_Y,
attention=attention,
gpu=gpu,
pretrained=pretrained,
load_model_filename=load_model_filename,
load_map_location=lambda storage, loc: storage)
if gpu:
machine = machine.cuda()
decode_method = "beam"
beam_size = 1
#max_beam_size = label_size
max_beam_size = None
#accum_logP_ratio_low = 0.1
#logP_ratio_low = 0.1
#agent = det_agent(max_beam_size, accum_logP_ratio_low, logP_ratio_low)
agent = None
# For German dataset, f_score_index_begin = 5 (because O_INDEX = 4)
# For toy dataset, f_score_index_begin = 4 (because {0: '<s>', 1: '<e>', 2: '<p>', 3: '<u>', ...})
f_score_index_begin = 4
# We don't evaluate on training set simply because it is too slow since we can't use mini-batch in adaptive beam search
val_fscore = machine.evaluate(val_X, val_Y, index2word, index2label, "val",
None, decode_method, beam_size,
max_beam_size, agent, f_score_index_begin)
time_begin = time.time()
test_fscore = machine.evaluate(test_X, test_Y, index2word, index2label,
"test", None, decode_method, beam_size,
max_beam_size, agent, f_score_index_begin)
time_end = time.time()
print_msg = "epoch %d, val F = %.6f, test F = %.6f, test time = %.6f" % (
epoch, val_fscore, test_fscore, time_end - time_begin)
log_msg = "%d\t%f\t%f\t%f" % (epoch, val_fscore, test_fscore,
time_end - time_begin)
print(print_msg)
print(log_msg, file=eval_output_file, flush=True)
eval_output_file.close()